Automatic quantification of cardiac MRI for hypertrophic cardiomyopathy

ABSTRACT

In one aspect the disclosed technology relates to embodiments of a method which, includes acquiring magnetic resonance imaging data, for a plurality of images, of the heart of a subject. The method also includes segmenting, using cascaded convolutional neural networks (CNN), respective portions of the images corresponding to respective epicardium layers and endocardium layers for a left ventricle (LV) and a right ventricle (RV) of the heart. The segmenting is used for extracting biomarker data from segmented portions of the images and, in one embodiment, assessing hypertrophic cardiomyopathy from the biomarker data. The method further includes segmenting processes for T1 MRI data and LGE MRI data.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to and incorporates entirely byreference corresponding U.S. Provisional Patent Application Ser. No.62/639,640 filed on Mar. 7, 2018, and U.S. Provisional PatentApplication Ser. No. 62/801,253 filed on Feb. 5, 2019, both entitled“Automatic Quantification of Cardiac MRI for Hypertrophic Cardiomyopathywith GPU.”

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

This invention was made with government support under Grant No.HL117006, awarded by The National Institutes of Health. The governmenthas certain rights in the invention.

FIELD

This disclosure relates to magnetic resonance imaging operations thatutilize convolutional neural networks to segment image data related to aheart of a subject.

BACKGROUND

Hypertrophic cardiomyopathy (HCM) is the most common monogenic heartdisease. It is characterized by unexplained left ventricularhypertrophy, myofibrillar disarray and myocardial fibrosis. While themajority of patients with HCM are asymptomatic, serious consequences areexperienced in a subset of affected individuals who may presentinitially with sudden cardiac death or heart failure.

Generally, HCM is a cardiovascular disease that affects the heartmuscle, also known as the myocardium. It causes thickening of the heartmuscle, especially in the ventricles (or lower heart chambers).Thickening of the myocardium occurs most commonly at the septum, whichis the muscular wall that separates the left and right side of theheart. The thickened septum may cause a narrowing that can block orreduce the blood flow from the left ventricle to the aorta—a conditioncalled “outflow tract obstruction.” The ventricles must pump harder toovercome the narrowing or blockage. HCM also may cause thickening inother parts of the heart muscle, such as the bottom of the heart calledthe apex, right ventricle, or throughout the entire left ventricle. Thedegree and location of thickening is fairly random. Myocardialthickening also causes cellular changes which leads to stiffening of thetissue. This restricts the normal relaxation of the ventricle, disablingit from fully filling with blood. Since there is less blood at the endof filling, there is less oxygen-rich blood pumped to the organs andmuscles, which leads to ischemia.

Currently known clinical risk markers are only modestly effective atidentifying those at highest risk. Currently the segmentation isperformed manually by experienced cardiologists due to the lack ofrobust automatic methods. As the number of cardiac magnetic resonance(CMR) images is very large (150-200 per patient), the process is verytime-consuming. The segmentations from a multi-person, multi-site studysuffer from a significant inter-observer variability as people differ intheir expertise and notion of the correct segmentation. Usually, onlythe end-systole and end-diastole phases of the cardiac cycle undergosegmentation as they reveal most of the information, and it would take apainfully long amount of time for humans to segment all the cardiacphases. This means that the bio-marker quantification is done solelybased on static information and no details corresponding to the motionof myocardium, which are of much significance, are considered.

Segmentation of magnetic resonance imaging (MRI) for HCM patients isparticularly challenging as they have much higher variability in shapeand size of the heart chambers. This is because the amount and place ofthickening of the myocardium is totally random. It would be verydifficult for a generic method to achieve good results. Some approachesthat perform both LV and RV segmentations treat them as two separateproblems, thus ignoring their relative positions and shapes. Avendi etal. [21] also proposed a technique to segment the RV. A dice score of0.81 was reported on the endocardium. Tran et al. [20] also suggestedtheir LV segmentation approach can be used, without any changes, tosegment the RV. Furthermore, none of these studies focus on HCMpopulations, and a model trained on normal and other patient populationsis very likely to perform poorly on an HCM dataset due to the notabledifferences in contrast and shape.

A need continues to exist in the art of magnetic resonance imaging for arobust automatic segmentation method could greatly reduce the cost andimprove the quality of CMR marker quantification. Moreover, an efficientautomatic segmentation method can be viewed as one segmenter, whichreduces the inter-observer variability. Also, a segmenting method andsystem disclosed herein can easily perform segmentation on all cardiacphases in a very short time, which is almost impossible for a human.This will allow diagnosticians, for the first time, to study the cardiacwall motion that could reveal interesting details pertaining to thedifferences in an HCM heart, in comparison with normal heart, that couldpotentially be a remarkable bio-marker.

SUMMARY

In one aspect the disclosed technology relates to embodiments of amethod which includes acquiring magnetic resonance imaging data, for aplurality of images, of the heart of a subject. The method also includessegmenting, using cascaded convolutional neural networks (CNN),respective portions of the images corresponding to respective epicardiumlayers and endocardium layers for a left ventricle (LV) and a rightventricle (RV) of the heart. The segmenting is used for extractingbiomarker data from segmented portions of the images and, in oneembodiment, assessing hypertrophic cardiomyopathy from the biomarkerdata.

In another aspect the method includes acquiring magnetic resonanceimaging data, for a plurality of images, of the heart of a subject. Themethod includes using a first set of cascaded convolutional neuralnetworks (CNN) operating with cine image data sets to segment respectiveportions of the plurality of images corresponding to respectiveepicardium layers and endocardium layers for a left ventricle (LV) and aright ventricle (RV) of the heart. A second set of cascadedconvolutional neural networks (CNN) operate on T1 image data sets tosegment additional images corresponding to the respective epicardiumlayer and endocardium layer for the LV of the heart. The method includesextracting biomarker data from segmented portions of the cine image datasets and the T1 image data sets and assessing hypertrophiccardiomyopathy from the biomarker data.

In another embodiment, the disclosed technology encompasses a systemhaving at least one processor and at least one memory device coupled tothe processor for storing computer-readable instructions which, whenexecuted by the at least one processor, cause the system to performfunctions of a method. The system implements a method of acquiringmagnetic resonance imaging data, for a plurality of images, of a heartof a subject, segmenting, using cascaded convolutional neural networks(CNN), respective portions of the images corresponding to respectiveepicardium layers and endocardium layers for a left ventricle (LV) and aright ventricle (RV) of the heart, and extracting biomarker data fromsegmented portions of the images. The method further includes assessinghypertrophic cardiomyopathy from the biomarker data.

In another embodiment of this disclosure, a non-transitorycomputer-readable medium has stored instructions that, when executed byone or more processors, cause a computing device to perform functions ofa method. The method includes acquiring magnetic resonance imaging data,for a plurality of images, of a heart of a subject, segmenting, usingcascaded convolutional neural networks (CNN), respective portions of theimages corresponding to respective epicardium layers and endocardiumlayers for a left ventricle (LV) and a right ventricle (RV) of theheart; extracting biomarker data from segmented portions of the images,and assessing hypertrophic cardiomyopathy from the biomarker data.

Other aspects and features according to the example embodiments of thedisclosed technology will become apparent to those of ordinary skill inthe art, upon reviewing the following detailed description inconjunction with the accompanying figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a system diagram illustrating an operating environment capableof implementing aspects of the disclosed technology.

FIG. 2 is a computer architecture diagram showing a general computingsystem capable of implementing aspects of the disclosed technology.

FIG. 3 illustrates a comparison of HCM (a, b) with a normal heart (c)and incorporates arrows pointing to the hypertrophy in myocardium asdiagnosed with the technology described herein.

FIG. 4 illustrates a series view of cine image data with twenty-fivecardiac phases/frames in sequence on a mid-cavity slice, in which phase7 is end-systole and end phase 25 is end-diastole imagery.

FIG. 5 illustrates a series of native T1 maps at basal, mid-sequence andapical slices in order from left to right.

FIG. 6 is a PRIOR ART representation of a 3D u-net architecture forconvolutional neural networks, as disclosed in Reference [26] citedbelow.

FIG. 7 is a schematic representation of workflow of the cascaded DCNNapproach for end-diastole cardiac chamber as disclosed herein.

FIG. 8 is a geometric representation of a method for division of a leftventricle of a subject's heart into six equal segments—anterior, anteroseptal, infero septal, inferior, infero lateral, antero lateralaccording to the methods disclosed herein.

FIG. 9 illustrates MRI images subject to segmenting according to thedisclosure herein for a) left ventricle (LV) and right ventricle (RV)Epicardium, b) right ventricle (RV) Endocardium, and c) left ventricle(LV) endocardium where ground truth contour is show in blue and use ofthe model output, according to the disclosure below, is set forth inyellow.

FIG. 10 illustrates a series of MRI images with automatic segmentationresults on cine data images for one patient at end-diastole according tothe disclosure herein.

FIG. 11 illustrates a series of MRI images with automatic (top) andmanual (bottom) segmentation on T1 maps.

FIG. 12 illustrates a series of MRI images with automatic (top) andmanual (bottom) segmentation on LGE images.

DETAILED DESCRIPTION

In some aspects, the disclosed technology relates to free-breathingparameter mapping with high-contrast image registration. Althoughexample embodiments of the disclosed technology are explained in detailherein, it is to be understood that other embodiments are contemplated.Accordingly, it is not intended that the disclosed technology be limitedin its scope to the details of construction and arrangement ofcomponents set forth in the following description or illustrated in thedrawings. The disclosed technology is capable of other embodiments andof being practiced or carried out in various ways.

It must also be noted that, as used in the specification and theappended claims, the singular forms “a,” “an” and “the” include pluralreferents unless the context clearly dictates otherwise. Ranges may beexpressed herein as from “about” or “approximately” one particular valueand/or to “about” or “approximately” another particular value. When sucha range is expressed, other exemplary embodiments include from the oneparticular value and/or to the other particular value.

By “comprising” or “containing” or “including” is meant that at leastthe named compound, element, particle, or method step is present in thecomposition or article or method, but does not exclude the presence ofother compounds, materials, particles, method steps, even if the othersuch compounds, material, particles, method steps have the same functionas what is named.

In describing example embodiments, terminology will be resorted to forthe sake of clarity. It is intended that each term contemplates itsbroadest meaning as understood by those skilled in the art and includesall technical equivalents that operate in a similar manner to accomplisha similar purpose. It is also to be understood that the mention of oneor more steps of a method does not preclude the presence of additionalmethod steps or intervening method steps between those steps expresslyidentified. Steps of a method may be performed in a different order thanthose described herein without departing from the scope of the disclosedtechnology. Similarly, it is also to be understood that the mention ofone or more components in a device or system does not preclude thepresence of additional components or intervening components betweenthose components expressly identified.

As discussed herein, a “subject” (or “patient”) may be any applicablehuman, animal, or other organism, living or dead, or other biological ormolecular structure or chemical environment, and may relate toparticular components of the subject, for instance specific organs,tissues, or fluids of a subject, may be in a particular location of thesubject, referred to herein as an “area of interest” or a “region ofinterest.”

Some references, which may include various patents, patent applications,and publications, are cited in a reference list and discussed in thedisclosure provided herein. The citation and/or discussion of suchreferences is provided merely to clarify the description of thedisclosed technology and is not an admission that any such reference is“prior art” to any aspects of the disclosed technology described herein.In terms of notation, “[n]” corresponds to the nth reference in thelist. For example, [5] refers to the 5th reference in the list, namelyHuang S, Liu J, Lee L C, et al. An image-based comprehensive approachfor automatic segmentation of left ventricle from cardiac short axiscine mr images. J Digit Imaging. 2011; 24(4):598-608. All referencescited and discussed in this specification are incorporated herein byreference in their entireties and to the same extent as if eachreference was individually incorporated by reference.

Generally, embodiments of the present disclosure provide, among otherthings, an automatic quantification of cardiac MRI (and related methodand computer readable media) for hypertrophic cardiomyopathy (HCM), and,optionally, with graphics processing units (GPU). In accordance with thequantification of HCM for diagnostic purposes, the embodiments shownherein are generally directed, without limitation to developing a fullyautomatic cascaded deep learning model to accurately segment bothepicardium and endocardium of left ventricles (LV) and right ventricles(RV) from cine images. The automation includes developing deep learningmodels to accurately segment the epicardium and endocardium of LV fromT1 images. Without limiting the disclosure to any particularembodiments, the segmenting protocols of this disclosure allow forquantifying at least biomarkers including, but not limited to, LV wallthickness, LV mass, RV mass, LV ejection fraction, RV ejection fraction,and mean myocardial T1.

A detailed description of aspects of the disclosed technology, inaccordance with various example embodiments, will now be provided withreference to the accompanying drawings. The drawings form a part hereofand show, by way of illustration, specific embodiments and examples. Inreferring to the drawings, like numerals represent like elementsthroughout the several figures.

FIG. 1 is a system diagram illustrating an operating environment capableof implementing aspects of the disclosed technology in accordance withone or more example embodiments. FIG. 1 illustrates an example of amagnetic resonance imaging (MRI) system 100, including a dataacquisition and display computer 150 coupled to an operator console 110,an MRI real-time control sequencer 152, and an MRI subsystem 154. TheMRI subsystem 154 may include XYZ magnetic gradient coils and associatedamplifiers 168, a static Z-axis magnet 169, a digital RF transmitter162, a digital RF receiver 160, a transmit/receive switch 164, and RFcoil(s) 166. The MRI subsystem 154 may be controlled in real time bycontrol sequencer 152 to generate magnetic and radio frequency fieldsthat stimulate magnetic resonance phenomena in a subject P to be imaged,for example, to implement magnetic resonance imaging sequences inaccordance with various example embodiments of the disclosed technologydescribed herein. An image of an area of interest A of the subject P(which may also be referred to herein as a “region of interest”) may beshown on display 158. The display 158 may be implemented through avariety of output interfaces, including a monitor, printer, or datastorage.

The area of interest A corresponds to a region associated with one ormore physiological activities in subject P. The area of interest shownin the example embodiment of FIG. 1 corresponds to a chest region ofsubject P, but it should be appreciated that the area of interest forpurposes of implementing various aspects of the disclosure presentedherein is not limited to the chest area. It should be recognized andappreciated that the area of interest in various embodiments mayencompass various areas of subject P associated with variousphysiological characteristics, such as, but not limited to the heartregion. Physiological activities that may be evaluated by methods andsystems in accordance with various embodiments of the disclosedtechnology may include, but are not limited to cardiac activity andconditions.

It should be appreciated that any number and type of computer-basedmedical imaging systems or components, including various types ofcommercially available medical imaging systems and components, may beused to practice certain aspects of the disclosed technology. Systems asdescribed herein with respect to example embodiments are not intended tobe specifically limited to magnetic resonance imaging (MRI)implementations or the particular system shown in FIG. 1 .

One or more data acquisition or data collection steps as describedherein in accordance with one or more embodiments may include acquiring,collecting, receiving, or otherwise obtaining data such as imaging datacorresponding to an area of interest. By way of example, dataacquisition or collection may include acquiring data via a dataacquisition device, receiving data from an on-site or off-site dataacquisition device or from another data collection, storage, orprocessing device. Similarly, data acquisition or data collectiondevices of a system in accordance with one or more embodiments of thedisclosed technology may include any device configured to acquire,collect, or otherwise obtain data, or to receive data from a dataacquisition device within the system, an independent data acquisitiondevice located on-site or off-site, or another data collection, storage,or processing device.

FIG. 2 is a computer architecture diagram showing a general computingsystem capable of implementing aspects of the disclosed technology inaccordance with one or more embodiments described herein. A computer 200may be configured to perform one or more functions associated withembodiments illustrated in one or more of FIGS. 3-12 . For example, thecomputer 200 may be configured to perform various aspects offree-breathing parameter mapping with high-contrast image registrationin accordance with example embodiments, such as magnetic resonanceimaging data acquisition, image registration, and calculating parametermaps. It should be appreciated that the computer 200 may be implementedwithin a single computing device or a computing system formed withmultiple connected computing devices. The computer 200 may be configuredto perform various distributed computing tasks, in which processingand/or storage resources may be distributed among the multiple devices.The data acquisition and display computer 150 and/or operator console110 of the system shown in FIG. 1 may include one or more systems andcomponents of the computer 200.

As shown, the computer 200 includes a processing unit 202 (“CPU”), asystem memory 204, and a system bus 206 that couples the memory 204 tothe CPU 202. The computer 200 further includes a mass storage device 212for storing program modules 214. The program modules 214 may be operableto perform associated with embodiments illustrated in one or more ofFIGS. 3-12 discussed below. The program modules 214 may include animaging application 218 for performing data acquisition and/orprocessing functions as described herein, for example to acquire and/orprocess image data corresponding to magnetic resonance imaging of anarea of interest. The computer 200 can include a data store 220 forstoring data that may include imaging-related data 222 such as acquireddata from the implementation of magnetic resonance imaging in accordancewith various embodiments of the disclosed technology.

The mass storage device 212 is connected to the CPU 202 through a massstorage controller (not shown) connected to the bus 206. The massstorage device 212 and its associated computer-storage media providenon-volatile storage for the computer 200. Although the description ofcomputer-storage media contained herein refers to a mass storage device,such as a hard disk or CD-ROM drive, it should be appreciated by thoseskilled in the art that computer-storage media can be any availablecomputer storage media that can be accessed by the computer 200.

By way of example and not limitation, computer storage media (alsoreferred to herein as “computer-readable storage medium” or“computer-readable storage media”) may include volatile andnon-volatile, removable and non-removable media implemented in anymethod or technology for storage of information such as computer-storageinstructions, data structures, program modules, or other data. Forexample, computer storage media includes, but is not limited to, RAM,ROM, EPROM, EEPROM, flash memory or other solid state memory technology,CD-ROM, digital versatile disks (“DVD”), HD-DVD, BLU-RAY, or otheroptical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other medium which canbe used to store the desired information and which can be accessed bythe computer 200. “Computer storage media”, “computer-readable storagemedium” or “computer-readable storage media” as described herein do notinclude transitory signals.

According to various embodiments, the computer 200 may operate in anetworked environment using connections to other local or remotecomputers through a network 216 via a network interface unit 210connected to the bus 206. The network interface unit 210 may facilitateconnection of the computing device inputs and outputs to one or moresuitable networks and/or connections such as a local area network (LAN),a wide area network (WAN), the Internet, a cellular network, a radiofrequency (RF) network, a Bluetooth-enabled network, a Wi-Fi enablednetwork, a satellite-based network, or other wired and/or wirelessnetworks for communication with external devices and/or systems. Thecomputer 200 may also include an input/output controller 208 forreceiving and processing input from any of a number of input devices.Input devices may include one or more of keyboards, mice, stylus,touchscreens, microphones, audio capturing devices, and image/videocapturing devices. An end user may utilize the input devices to interactwith a user interface, for example a graphical user interface, formanaging various functions performed by the computer 200. The bus 206may enable the processing unit 202 to read code and/or data to/from themass storage device 212 or other computer-storage media. Thecomputer-storage media may represent apparatus in the form of storageelements that are implemented using any suitable technology, includingbut not limited to semiconductors, magnetic materials, optics, or thelike. The computer-storage media may represent memory components,whether characterized as RAM, ROM, flash, or other types of technology.

The computer storage media may also represent secondary storage, whetherimplemented as hard drives or otherwise. Hard drive implementations maybe characterized as solid state, or may include rotating media storingmagnetically-encoded information. The program modules 214, which includethe imaging application 218, may include instructions that, when loadedinto the processing unit 202 and executed, cause the computer 200 toprovide functions associated with one or more example embodiments andimplementations illustrated in FIGS. 3-12 . The program modules 214 mayalso provide various tools or techniques by which the computer 200 mayparticipate within the overall systems or operating environments usingthe components, flows, and data structures discussed throughout thisdescription.

In general, the program modules 214 may, when loaded into the processingunit 202 and executed, transform the processing unit 202 and the overallcomputer 200 from a general-purpose computing system into aspecial-purpose computing system. The processing unit 202 may beconstructed from any number of transistors or other discrete circuitelements, which may individually or collectively assume any number ofstates. More specifically, the processing unit 202 may operate as afinite-state machine, in response to executable instructions containedwithin the program modules 214. These computer-executable instructionsmay transform the processing unit 202 by specifying how the processingunit 202 transitions between states, thereby transforming thetransistors or other discrete hardware elements constituting theprocessing unit 202. Encoding the program modules 214 may also transformthe physical structure of the computer-storage media. The specifictransformation of physical structure may depend on various factors, indifferent implementations of this description. Examples of such factorsmay include, but are not limited to, the technology used to implementthe computer-storage media, whether the computer storage media arecharacterized as primary or secondary storage, and the like. Forexample, if the computer storage media are implemented assemiconductor-based memory, the program modules 214 may transform thephysical state of the semiconductor memory, when the software is encodedtherein. For example, the program modules 214 may transform the state oftransistors, capacitors, or other discrete circuit elements constitutingthe semiconductor memory.

As another example, the computer storage media may be implemented usingmagnetic or optical technology. In such implementations, the programmodules 214 may transform the physical state of magnetic or opticalmedia, when the software is encoded therein. These transformations mayinclude altering the magnetic characteristics of particular locationswithin given magnetic media. These transformations may also includealtering the physical features or characteristics of particularlocations within given optical media, to change the opticalcharacteristics of those locations. Other transformations of physicalmedia are possible without departing from the scope of the presentdescription, with the foregoing examples provided only to facilitatethis discussion.

Example Implementations and Results

Various aspects of the disclosed technology may be still more fullyunderstood from the following description of example implementations andcorresponding results and the images of FIGS. 3-12 . Some experimentaldata are presented herein for purposes of illustration and should not beconstrued as limiting the scope of the disclosed technology in any wayor excluding any alternative or additional embodiments.

There are several challenges in automating the heart chambersegmentation task, namely, the heterogeneities in the brightness of LVcavity due to blood flow and the presence of papillary muscles withsignal intensities similar to myocardium make it harder to delineate theendocardial wall. Tissues surrounding the epicardium (fat, lung), whichhave different intensity profiles and show poor contrast with themyocardium, make the segmentation of the epicardial difficult.Segmentation complexity also depends on the slice level of the image.Apical and basal slice images are more difficult to segment thanmid-ventricular images. Indeed, MRI resolution is not high enough toresolve the size of small structures at the apex and ventricle shapesare strongly modified close to the base of the heart, because of thevicinity of the atria [1]. Also, MRI data suffers from inherent noisedue to the bias in magnetic field. Dynamic motion of heart causesinhomogeneity of intensity and high variations in contrasts. Anirregular crescent shape of the right ventricle makes it much harder tosegment in comparison with the left ventricle. Moreover, data from theknown HCM population has a much higher variability in the shape and sizeof the heart chambers because of the randomness of hypertrophy incomparison with normal and other pathologies. In this regard, FIG. 3shows basal, mid ventricular and apical slices from HCM hearts (a, b)and a normal heart (c). The myocardial hypertrophy is pointed to bywhite arrows. It can be observed that in a normal heart the wallthickness is consistent throughout the LV, whereas in HCM hearts theconsistency highly varies.

In deciphering the biomarkers noted above to diagnose heartirregularities pointing to HCM, this disclosure refers to both Cine CMRimages (referred to as cine images) and T1 images, both discussed below.The procedures also use standard reference images from available HCMRegistries [24] to train, test, and augment MRI data collection inaccordance with standards in the industry.

Cine images are short movies that are able to show heart motionthroughout the cardiac cycle, in short-axis. Measurement of leftventricular (LV) mass, ejection fraction, percentage of LV masssubtended by scar, and extracellular volume is critical toidentification of HCM patients at highest risk. Information from cineimages can be very helpful in efficient quantification of such biomarkers. To achieve accurate measurements of these variables,segmentation of heart chambers and myocardium regions is required on CMRimages.

Cine images are obtained with electrocardiogram (ECG) triggeredsegmented imaging. Segmented acquisition is the process of dividing thecardiac cycle into multiple segments to produce a series of images thatcan be displayed as a movie. The cardiac cycle begins with an R wave ofthe electrocardiogram, ends with the subsequent R wave and is typicallydivided into 10 to 20 segments, depending on the heart rate. Withoutlimiting this disclosure to any single example, each image in the cineis typically composed of information gathered over several heart beatsallowing for a movie to be acquired with a breath hold of 10 to 20seconds, depending on the sequence. As in any movie, the final cine is asequence of individual frames. These images can be very helpful instudying cardiac function, valvular function, and movement of bloodthrough the heart. In the cardiac cycle, which is comprised of multiplephases represented by different frames in the cine, this disclosureexamines two phases, namely end-diastole and end-systole. Atend-diastole, the myocardium is completely relaxed and is fully filledwith blood that will be pumped in the following systole phase. Atend-systole, the myocardium is completely contracted and has pumped allthe blood it can, out of the ventricle. To illustrate the usefulness ifcine image data, FIG. 4 shows the cine on one slice, the cine includesend-systole and end-diastole which are marked correspondingly.

In other embodiments disclosed herein, processes for MRI diagnosticpurposes use T1 data as an output from the imaging procedures.Embodiments of this disclosure illustrate the use of native T1 maps tosegment out the left ventricle (LV). The LV epicardial and endocardialground truth contours are available from known HCM Registry data for allcorresponding images. Data preprocessing is fairly similar to that ofcine data. As T1 maps are only acquired at basal, mid-cavity and apicalpositions rather than on all slices that cover the entire heart, theprocedures herein use 2D versions of data augmentations to warp each 2Dimage. To illustrate basic T1 data, FIG. 5 illustrates native T1 maps atbasal, mid-sequence and apical slices along with the epi and endocardial(red and green respectively) contours.

Beginning with cine data segmentation, a single model is used to segmentboth the LV and RV epicardium with three labels of output: background,LV chamber and RV chamber. During training of the convolutionalprotocol, random 3D affine transformation including translation,rotation, scaling and shear was used on the fly to augment the dataset.Adam optimizer is used with a learning rate of 5e-4 to minimize theloss. The model was trained for 800 epochs on a TitanX GPU for 8 hrs.For both LV and RV, only one fully connected component is retainedduring post-processing.

Endocardium Segmentation—Diastole

As the endocardium is always inside the epicardium, to maximize theefficiency of the network by focusing on the myocardium-blood contrast,two separate image models are trained for LV and RV endocardialsegmentation on masked images. Afterward, the segmentation task is apixel-wise binary classification problem.

During training, ground truth epicardium masks are used to obtain atight bounding box that encloses the contour. The images are thencropped accordingly and pixels outside the epicardium are also maskedout. As a comparison, the protocols used herein also trained modelswithout images to check their contribution to the performance. Thecropped images are then resized to 128×128×32 dimensions to ensure thatall inputs to the model have the same size. Similar affine augmentationsis performed as well. During testing, results from the correspondingepicardium segmentation generated by the model are used for masking andbounding box extraction.

Adam optimizer was used with a learning rate of 5e-4 to minimize theloss on both models which were trained for 400 epochs on a TitanX GPUfor 4 hrs each.

Endocardium Segmentation—Systole

Although training augmentation of the images at end-diastole phase canmimic end-systole images for general cardiac MRI, results discussedbelow show that the model trained with end-diastole images performspoorly on end-systole images for HCM patients since the shape andcontrast is drastically different between the two phases. In someextreme cases where wall thickening is severe, no blood signal isvisible, yet the chamber boundary still should be drawn. As ground truthepicardium masks are not available at systole, the cascaded approach isoften unavailable. Individual models were then used for the endosegmentation of LV and RV on the original images. Adam optimizer wasused with a learning rate of 5e-4 to minimize the loss on both modelswhich were trained for 200 epochs on a Titan X GPU for 4 hours in total.

T1 Segmentation

Segmentation of T1 maps is fairly straight forward and highly similar tothe strategy followed for systole endocardium. Training two differentmodels with the computerized methods of this disclosures provides onemodel to segment LV epicardium and the other model to segment LVendocardium. While segmenting the endocardium, the results belowproceeded from masking the input image with the results of epicardiumsegmentation. Adam optimizer was used with a learning rate of 5e-4 tominimize the loss on both models which were trained for 200 epochs on aTitan X GPU for 4 hours in total.

Biomarker Quantification

In addition to ejection fraction and LV, RV mass, abnormal wallthickening is one characteristic of HCM. Therefore, regional wallthickness on different myocardium segments needs to be calculated forfurther analysis.

Wall Thickness

Wall thickness calculations are made by using all slices, includingapical, and dividing the same into 6 segments. FIG. 8 shows the divisionof left ventricle into six segments of 60° each on a mid-ventricularslice. Segments 2 and 3 are first found by identifying the septum, whichis the overlap of LV and RV epi contours. The exact boundaries of thetwo segments are symmetrically adjusted to make sure the angle is 120°.Segments 1, 6, 5, 4 can be found by dividing the remaining area intofour equal parts. Myocardium thickness is calculated at the beginning ofeach segment—represented by solid white lines in FIG. 8 . The sameangles are used for all other slices for myocardium division.

Ejection Fraction, LV & RV Mass

Simpson's rule is used to calculate endocardial volumes at end-diastole(EDV) and end-systole (ESV). Ejection fraction (EF) can be found by thebelow formula. EFs of both LV and RV are calculated.

${EF} = {\frac{{EDV} - {ESV}}{EDV}*100}$

Mass calculations require epicardial volumes in addition to endocardialvolumes. As epi contours are not available at end-systole, mass is onlycalculated at end-diastole. Myocardial volume is calculated as thedifference of epi and endocardial volumes. Mass is calculated as theproduct of myocardial volume and density, which was assumed to beconstant at 1.05 g/cc.

Mean Myocardial T1

Changes in myocardial T1 can be a very helpful biomarker in identifyingrisk associated with HCM. It is calculated by taking the average of allthe pixel values that lie in the myocardium, which is identified fromthe corresponding segmentation masks on T1 maps.

One non-limiting goal of this work is to automate the rapid and robustquantification of biomarkers including left and right ventricular mass,ejection fraction and myocardial wall thickness, from cine images, andmean myocardial T1 from native T1 maps for identification of HCMpatients at risk. To achieve accurate measurements of these variables,segmentation of heart chambers and myocardium regions is required oncine images across all short-axis slices and for at least end-diastoleand end-systole phases, and on native T1 maps. Currently thesegmentation is performed manually by experienced cardiologists, so itis time-consuming and suffers from inter-observer variability withreduced biomarker quantification accuracy and robustness, especially ina multi-site study. Automating the biomarker quantification involves anautomatic heart chamber segmentation task.

Existing automatic cardiac MRI segmentation techniques can broadly beclassified into two groups—ones that need no prior information [2-7] andones that need weak or strong prior knowledge [8-10]. The formerincludes techniques that are primarily image-driven which use the pixelintensity differences to distinguish between different regions ofinterest. The latter techniques are often model-driven, usingstatistical information extracted from manually annotated training datathat describe the spatial relationships of the LV and RV objects andtheir surrounding structures, knowledge of the heart biomechanics, oranatomical assumptions about the statistical shapes of the objects. Suchassumptions about the objects, through either weak or strong priors,contributes to the propensity of these methods to overfit on aparticular training dataset, thus making them less generalizable.

Among the approaches that need no prior information, which includesactive contours or snakes techniques, pixel classification usingclustering algorithms, region growing algorithms, and learning basedtechniques, accurate fully automatic segmentation is only achievableusing learning based techniques. These include techniques based onrandom forests [11-13], markov random fields [5-14] (MRF), conditionalrandom fields [15, 16] (CRF), restricted boltzman machines [17] (RBM)and deep learning. Methods using random forests rely on image intensityand define the segmentation problem as a classification task. Thesemethods have multiple stages of intensity standardization, estimationand normalization, which are computationally expensive and affect thesuccess of further steps. Moreover, their performance is rather mediocreat basal and apical slices and overall inferior to the state-of-the-art.MRFs and RBMs try to learn the probability of input data. Computing theimage probability and parameter estimation in generative models isgenerally difficult and can lead to reduced performance ifoversimplified. Besides, they use the Gibbs sampling algorithm fortraining, which can be slow, become stuck for correlated inputs, andproduce different results each time it is run due to its randomizednature. Alternatively, CRF methods try to model the conditionalprobability of latent variables, instead of the input data. However,they are still computationally difficult, due to complexity of parameterestimation, and their convergence is not guaranteed. Deep learningtechniques, that received much attention over the course of last 5years, are much more stable and achieve a better performance incomparison with the techniques mentioned earlier.

Processes discussed herein take advantage of image analysis by use ofconvolutional neural networks (CNNs). CNNs are multi-layer feed-forwardnetworks specifically designed to recognize features in image data. Atypical application of CNNs consists of recognition of various objectsin images. However convolutional networks have been successfully usedfor various different tasks, too. The neurons in CNNs work byconsidering a small portion of the image, referred to herein as a patch.The patches are inspected for features that can be recognized by thenetwork. As a simple example, a feature may be a vertical line, an arch,or a circle. These features are then captured by the respective featuremaps of the network. A combination of features is then used to classifythe image, or in our case, each pixel.

Deep convolutional neural networks (DCNN) have shown great promise inmany medical image segmentation tasks, including cardiac MRI. A majorityof these only focus on segmenting the LV for ejection fractioncalculation. Yang et al. [18] proposed a fully convolutionalarchitecture to segment the LV myocardium which is relatively shallow,consisting of three convolutional blocks with two of them followed bymax pooling and one 4 stride deconvolution to regain the original imagedimension. An average of dice score of 0.75 was reported on the CMRdataset from York University, Avendi et al. [19], proposed a hybridapproach that uses deformable models in conjunction with deep learning.A fully convolutional network is used to locate the LV, a stacked autoencoder is then used to infer the shape of the ventricle which is thenused by a deformable model to accurately segment the region of interest.The main limitation of this method is that it is multi-stage andrequires manual offline training along with extensive hyper-parametertuning, which can be cumbersome and difficult to generalize tomulti-site data. Tran et al. [20] proposed a 15-layered architecturethat uses 2D data, which achieved the state-of-art dice scores of 0.96on epicardium and 0.92 on endocardium using the Sunnybrook dataset.Despite being the state-of-art, this technique uses 2D data, ignoringthe overall shape of the LV which could be crucial in identifying theedge slices that shouldn't be segmented.

Although efficient, using the simple convolutional operation might notyield the best feature extraction at times when the regions of interesthave complex shapes. Moreover, using larger kernels to increase theeffective receptive field (the dimension of the original image viewed bya convolutional layer) size so as to infer more spatial information isnot always efficient as there will be a lot more parameters to trainwhich not only takes longer, but could also lead to overfitting. Toovercome these issues, this disclosure includes incorporating dilatedconvolutions into the appropriate CNN.

Dilated Convolution

In general, the receptive field of the CNN should be larger the regionof interest being segmented so as to acquire enough spatial information.A simple way of increasing the receptive field is to use a largerconvolutional kernel. But doing so also increases the number ofparameters that are to be trained which not only increases the time toconvergence of the gradient descent algorithm but also increases thechance of overfitting on the training data. To overcome this issue theidea of dilated convolutions [22] was put forth. In simple terms,dilated convolution is a convolution applied to input with defined gaps.With this definition, given our input is an 2D image, dilation rate k=1is normal convolution and k=2 means skipping one pixel per input and k=4means skipping 3 pixels.

The training phase of a CNN involves calculation of the loss term andback propagation of the loss through the entire network. The loss termrepresents the error in prediction made by the CNN on an input. Thegradients computed on each layer represent the contribution of thatlayer to the final loss term. When back propagating, all the trainableparameters are updated according to their gradients. When this isrepeated on all training inputs for several epochs, the parameters willbe updated in a way that they approximate a non-linear function thatmodels the task at hand.

Segmentation Using CNNs

Image segmentation using CNNs is a classification task on a pixel level.Fully Convolutional Networks (FCN)(CNNs with all convolutional layers)popularized CNN architectures for dense predictions. This allowedsegmentation maps to be generated for image of any size. Almost all thesubsequent state of the art approaches on segmentation adopted thisparadigm. One of the main problems with using CNNs for segmentation ispooling layers. Pooling layers increase the field of view and are ableto aggregate the context while discarding the ‘where’ information.However, segmentation requires the exact alignment of class maps andtherefore, needs the ‘where’ information to be preserved.Encoder-Decoder style network architecture was proposed to tackle thisissue. Encoder gradually reduces the spatial dimension with poolinglayers and decoder gradually recovers the object details and spatialdimension. There are usually shortcut connections from encoder todecoder to help decoder recover the object details better.

3D UNet

3D UNet was originally proposed by Cicek et al. [23] for automaticsegmentation of Xenopus (a highly aquatic frog) kidney. It has anencoder-decoder style architecture with skip connections betweencorresponding layers in encoding and decoding paths. This architectureis very popular for medical image segmentation. All the deep learningmodels used in this study have the same architecture, the 3D UNet. 3D inthe name indicates that the input to this network is a 3D image. UNetrefers to the structure of the network, which resembles the letter ‘U’.FIG. 6 shows the block representation of 3D UNet architecture.

Each convolutional block has two convolutions followed by max pooling.Every convolution is immediately followed by a rectified linear unit(ReLU) activation and batch normalization layer. Each deconvolutionalblock consists of two convolutions followed by a deconvolution to regainspatial dimension. Moreover, there are skip connections from theencoding path to decoding path at corresponding spatial dimensions.These are shown by green arrows. The very final convolution (shown by apurple arrow) that generates a three-dimensional feature map is followedby a softmax activation in order to obtain a pseudo-random probabilitydistribution at each pixel representing its class membership. All thedeep learning models used in this work have the UNet architecture

A segmenting procedure according to this disclosure is adapted to HCMdiagnosis, in part by the workflow shown in FIG. 7 of the attacheddrawings. A cascaded deep learning based approach was developed toaccurately segment the heart chambers and thereby automate thequantification of HCM biomarkers. First, accurate segmentations for LVand RV epicardium are obtained with one network. Results from the episegmentation are then used to obtain tight bounding boxes that enclosesthe LV and RV chamber, respectively. Separate models are trained forendocardium segmentation for each chamber. Input images are also maskedby the results from epi segmentation so that the network can focus onthe inside of the chamber. The following sub sections further discusseach step of the workflow shown in FIG. 7 .

DCNN Architecture and Loss Metric

A 3D-UNet style architecture is used for the segmentation in both steps.Convolutions in the encoding phase of 3D-UNet were replaced with dilatedconvolutions to increase the receptive field without having to trainmore parameters. In medical images, the ground truth masks are dominatedby background leading to an overwhelming class imbalance, especiallywhen there are multiple foreground classes.

This can be addressed by applying a weight map to the categorical-crossentropy loss function or by using a dice-similarity metric-based lossfunction [13]. The latter is usually preferred as it does not rely onhyper parameters. With multiple foreground labels, the loss is given as:

${Loss} = {1 - \frac{\Sigma_{i = 1}^{n}{Dice}_{i}}{n}}$

where n is the number of classes, excluding background and eachindividual dice is calculated using Sorenson's coefficient.

Evaluation

Dice scores and average perpendicular distance (APD) are calculated toevaluate the segmentation quality. To demonstrate the necessity ofdifferent models for HCM patients, the procedure also trained a modelfor LV epicardium and endocardium segmentation using the SunnyBrookdataset, which contains normal and other patient populations, and testedthe model on HCM patients.

Dice Score

The dice score is a statistic used for comparing the similarity of twosamples. When applied to boolean data, using the definition of truepositive (TP), false positive (FP), and false negative (FN), it can bewritten as:

${DSC} = {\frac{2TP}{{2{TP}} + {FP} + {FN}}.}$The value of dice score ranges from 0 to 1 with 0 being completemismatch and 1 being perfect overlay.

Average Perpendicular Distance

The average perpendicular distance (APD) measures the distance from theautomatically segmented contour to the corresponding manually drawnexpert contour, averaged over all contour points. A high value impliesthat the two contours do not match closely. In general, and APD valueless than 5 mm is considered a good contour. Considering a pixel spacingof 1 mm, the APD for the above example is 1.426 mm, maximum distance is5.45 mm and minimum distance is 0 mm.

Symmetric Mean Absolute Percentage Error

Symmetric mean absolute percentage error (sMAPE) is an accuracy measurebased on percentage (or relative) errors. It is used to evaluate thequantification of biomarkers. For a set of actual values A and predictedvalues P, sMAPE is given by

${sMAPE} = {\frac{100\%}{n}{\sum\limits_{t = 1}^{n}\frac{❘{P_{t} - A_{t}}❘}{\left( {{❘P_{t}❘} + {❘A_{t}❘}} \right)}}}$within the meaning and range of equivalents thereof are intended to beembraced therein.

Results

Results for segmentation of epicardium and endocardium of both LV and RVare shown in FIG. 9 with ground truth contours in blue and the modeloutput in yellow. FIG. 9 a shows the combined LV and RV epicardium, 9 bthe RV endocardium and 9 c the LV endocardium. Alongside this, theaverage dice scores for these ROIs is reported in Table 1 set forthherein.

TABLE 1 Average DICE scores Combined LV, RV Epi Endo-LV Endo-RV 0.89910.8788 0.7621

The following material illustrates a brief review of the steps describedin this disclosure to achieve the results discussed herein:

In a first dataset of cine image data, the procedure included selecting69 patients from a single institution with manually drawn and verifiedsegmentation for epicardial and endocardial contours of LV and RV. Thecoverage was 10-15 short-axis slices with 8 mm slice thickness coveringentire LV and RV. The imaging biomarkers retrieved were LV and RV mass,ejection fraction and wall thickness at different segments. FIG. 10shows the automatic segmentation results for one patient at enddiastole.

In a second dataset of T1 image data, the procedure included selected 67patients from two institutions with verified segmentation for epicardialand endocardial contours of LV with 1-4 T1 maps (pre-contrast, 5 min, 14min, 29 min post-contrast). The coverage included 3 short-axis slices atapex, mid-ventricle, and base. The resulting imaging biomarker was amean T1 value. FIG. 11 shows the automatic (top) and manual (bottom)segmentation on T1 maps.

In a third dataset that included images with Late Gadolinium Enhancement(LGE), the procedure included selecting 63 patients from a singleinstitution with verified epicardium and endocardium contours of LV.[27]. FIG. 12 shows the automatic (top) and manual (bottom) segmentationon LGE.

The segmentation of the above noted data sets included a deepconvolutional neural network structure (DCNN) with a 2-dimensional U-Nethaving dilated convolutions to increase the receptive field withoutintroducing more parameters. The workflow generally included, but is notlimited to, pre-processing by unifying the spatial resolution and matrixsize. For cine data, the procedures utilized separate networks to trainepicardial (LV and RV) and endocardial contours. One should note thatuse of a DCNN included training augmentation, such as but not limitedto, random b-spline based deformation and affine transformation. Testingaugmentation included, but is not limited to, rotating the input imagesand averaging the output. Post-processing steps included, but are notlimited to, removing isolated regions and combining the results fromepicardial and endocardial contours.

Results of the segmenting analysis allows for biomarker extraction fordiagnostic purposes. Upon calculating LV and RV volume, mass, andejection fraction, the procedure continues by summing up all therelevant pixels and multiplying by the voxel size (partial volume effectis ignored) to calculate the volume before calculating the correspondingmass or ejection fraction variable. In certain embodiments, the resultsdescribed herein are achieved by using a first set of cascadedconvolutional neural networks (CNN) operating with cine image data setsto segment respective portions of the plurality of images correspondingto respective epicardium layers and endocardium layers for a leftventricle (LV) and a right ventricle (RV) of the heart and using asecond set of cascaded convolutional neural networks (CNN) operating onT1 image data sets to segment additional images corresponding to therespective epicardium layer and endocardium layer for the LV of theheart.

Along those lines, the method includes using the first set of cascadedconvolutional neural networks (CNN) to segment cine image data sets by(i) applying a first cine data CNN to first selected image datarepresenting the LV and the RV epicardium portions of the heart; (ii)applying a second cine data CNN to second selected image datarepresenting the LV endocardium portion of the heart; and (iii) applyinga third cine data CNN to third selected image data representing the RVendocardium portion of the heart.

Using the second set of cascaded convolutional neural networks (CNN) tosegment T1 image data may include (i) applying a first T1 data CNN tofourth selected image data representing the LV epicardium portion of theheart; and (ii) applying a second T1 data CNN to fifth selected imagedata representing the LV endocardium portion of the heart.

As noted briefly above, one of the goals of this disclosure is toaccurately determine LV wall thickness for HCM diagnosis. FIG. 8 of theattached drawings shows one non-limiting embodiment of calculating wallthickness by automatically dividing the myocardium into six segments(1-6). Next, the cine image data is subject to the steps of thisdisclosure, that identify the two RV insertion points, by obtaining theintersection area (810) of LV and RV epicardial contours and getting theboundary points (825A, 825B). The image data is then analyzed with theabove noted CNN to find the center of mass (830) of the LV. In ageometrical operation, the image data is manipulated to identify abisector line (840) of two additional lines (850, 860) connecting the LVcenter of mass (830) to the two insertion points, respectively. Finally,the method of this disclosure rotates the bisector by 60°, 120°, 180°,240°, 300° to get all 6 lines and calculate their intersections to theLV epicardium (870) and endocardium (880) contours.

Using the above noted quality and accuracy calculations on the datashows that the cine segmentation exhibits the following for quality andaccuracy as shown in Table 2:

TABLE 2 Dice and MSD for different regions and different phases (ED:end-diastole, ES: end-systole) on cine images Dice MSD (mm) LV epi-ED0.939 ± 0.028 2.234 +/− 1.630 LV-endo-ED 0.937 ± 0.024 1.708 +/− 1.239RV-epi-ED 0.861 ± 0.041 2.609 +/− 0.957 RV-endo-ED 0.859 ± 0.043 2.579+/− 1.228 LV-endo-ES 0.822 ± 0.071 2.469 +/− 1.525 RV-endo-ES 0.750 ±0.127 3.152 +/− 1.911

T1 & LGE segmentation quality and accuracy are summarized in Table 3:

TABLE 3 Dice scores for epi and endo contours on T1 maps and LGE imagesDice T1-epi 0.938 +/− 0.036 T1-endo 0.916 +/− 0.046 LGE-epi 0.926 +/−0.044 LGE-endo 0./920 +/− 0.041 

Biomarker quantification quality and accuracy:

TABLE 4 Mean absolute percentage error (MAPE) of imaging biomarkers MAPE(%) LV-mass 11.41 +/− 8.64  LV-EF 9.49 +/− 7.41 RV-mass 26.95 +/− 25.61RV-EF 11.35 +/− 10.56 LV-mean thickness 18.06 +/− 7.68  LV-mean T1 1.55+/− 1.85

This disclosure utilizes, in part the following validation protocolsteps by randomly splitting the image dataset using a 3:2 ratio fortraining and validation on the patient level. The Dice score iscalculated over the whole image stack, and mean surface distance (MSD)is calculated over each slice. To complete biomarker quantification, theprotocol of this disclosure includes, but is not limited to, using thesame extraction method on the ground-truth contours and theautomatically segmented contours. Mean absolute percentage error (MAPE)is then calculated for the biomarkers across a set “n” of actualbiomarker values versus the corresponding forecast values provided bythe automated procedures as follows:

${M = {\frac{100\%}{n}{\sum\limits_{t = 1}^{n}{❘\frac{A_{t} - F_{t}}{A_{t}}❘}}}},$

Table 5 summarizes the results of biomarker quantification. Forcomparison, root mean square error (RMSE) values from model predictionsand an inter-observer study on generic cardiac MRI data are reported[15]. Wall thickness measurements are only done at end-diastole as noepicarcial contours are available for end-systole. The inter observerRMSE values reported in Table 5 are from a population that isrepresentative of healthy patients. [25]

TABLE 5 Quantification results for LV, RV Mass, Ejection Fraction, Meanmyocardial T1 and the End-Diastole wall thickness. InterObserver_ sMAPERMSE (Generic Biomarker in % Model_RMSE Cardiac MRI) LV Mass 13.7 ± 8.9 52.4 gm 17.5 gm RV Mass 32.2 ± 18.9 16.4 gm N/A LV Ejection Fraction 5.8± 4.5  9.5% 4.2% RV Ejection Fraction 9.2 ± 5.9 21.6% N/A Wall Thickness20.8 ± 8.1  2.97 mm N/A Mean Myocardial T1 2.91 ± 2.96  54.5 msec N/A

RMSE values on HCM population are expected to be higher given theincreased variability in the size and shape of heart chambers. Highererrors in RV related values are a result of poor segmentation incomparison with LV. Moreover, the poor performance in basal slicesegmentation contributes significantly to the errors in mass andejection fraction calculations. For myocardial T1, in general the valuesare around 1000 msecs. RMSE of 54.5 msec and sMAPE of 2.9% indicates arobust quantification.

CONCLUSION

The specific configurations, choice of materials and the size and shapeof various elements can be varied according to particular designspecifications or constraints requiring a system or method constructedaccording to the principles of the disclosed technology. Such changesare intended to be embraced within the scope of the disclosedtechnology. The presently disclosed embodiments, therefore, areconsidered in all respects to be illustrative and not restrictive. Thepatentable scope of certain embodiments of the disclosed technology isindicated by the appended claims, rather than the foregoing description.

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What is claimed is:
 1. A method, comprising: acquiring magneticresonance imaging data, for a plurality of images, of the heart of asubject; segmenting, using cascaded convolutional neural networks (CNN),respective portions of the images corresponding to respective epicardiumlayers and endocardium layers for a left ventricle (LV) and a rightventricle (RV) of the heart; extracting biomarker data from segmentedportions of the images; and assessing hypertrophic cardiomyopathy fromthe biomarker data.
 2. A method according to claim 1, wherein assessinghypertrophic cardiomyopathy comprises extracting biomarker datacomprising a LV wall thickness measurement.
 3. A method according toclaim 1, further comprising segmenting the respective epicardium layerswith a first convolutional neural network of the cascaded convolutionalneural networks prior to segmenting the endocardium layers.
 4. A methodaccording to claim 3, further comprising using segmented epicardiumlayers to automatically select a region of interest for segmenting theendocardium layers.
 5. A method according to claim 4, further comprisingsegmenting respective endocardium layers for the LV and the RV byrespective second and third convolutional neural networks of thecascaded convolutional neural networks on the region of interest.
 6. Amethod according to claim 1, wherein extracting biomarker data comprisescalculating a volume quantity for the LV and the RV.
 7. A methodaccording to claim 6, further comprising calculating a mass quantity andan ejection fraction for the respective LV and RV from the volumequantity.
 8. A method according to claim 1, wherein extracting biomarkerdata comprises calculating an LV wall thickness by identifying two RVinsertion points within the images of the LV by obtaining anintersection area of LV and RV epicardial contours and setting boundarypoints for the contours within the image data.
 9. A method according toclaim 8, wherein calculating the LV wall thickness further comprisescalculating a position within the image data representing a center ofmass of the LV.
 10. A method according to claim 9, wherein calculatingthe LV wall thickness further comprises identifying two respective linesof image data connecting the LV center of mass to the two RV insertionpoints and identifying at least one bisector line extending between therespective lines of image data and from the LV center of mass toward theintersection area of LV and RV epicardial contours.
 11. A methodaccording to claim 10, wherein calculating the LV wall thickness furthercomprises identifying additional segmenting lines and calculating the LVwall thickness from intersections of the segmenting lines with theepicardium contours and the endocardium contours of the LV.
 12. A methodaccording to claim 11, wherein the additional segmenting lines arepositioned on the images of the LV wall to define angles with the atleast one bisector line, and wherein the angles have increments aroundthe LV wall of about sixty degrees.
 13. A method according to claim 1,further comprising standardizing the plurality of images to a selectednumber of layers and a preset pixel matrix size.
 14. A method,comprising: acquiring magnetic resonance imaging data, for a pluralityof images, of the heart of a subject; using a first set of cascadedconvolutional neural networks (CNN) operating with cine image data setsto segment respective portions of the plurality of images correspondingto respective epicardium layers and endocardium layers for a leftventricle (LV) and a right ventricle (RV) of the heart; using a secondset of cascaded convolutional neural networks (CNN) operating on T1image data sets to segment additional images corresponding to therespective epicardium layer and endocardium layer for the LV of theheart; extracting biomarker data from segmented portions of the cineimage data sets and the T1 image data sets; and assessing hypertrophiccardiomyopathy from the biomarker data.
 15. A method according to claim14, wherein using the first set of cascaded convolutional neuralnetworks (CNN) to segment cine image data sets further comprises: (i)applying a first cine data CNN to first selected image data representingthe LV and the RV epicardium portions of the heart; (ii) applying asecond cine data CNN to second selected image data representing the LVendocardium portion of the heart; and (iii) applying a third cine dataCNN to third selected image data representing the RV endocardium portionof the heart.
 16. A method according to claim 14, wherein using thesecond set of cascaded convolutional neural networks (CNN) to segment T1image data comprises: (i) applying a first T1 data CNN to fourthselected image data representing the LV epicardium portion of the heart;and (ii) applying a second T1 data CNN to fifth selected image datarepresenting the LV endocardium portion of the heart.
 17. A methodaccording to claim 16, further comprising masking the T1 image data ofthe heart with segmented image data representing the LV epicardiumportion of the heart before applying the second T1 data CNN to the LVendocardium portion of the heart.
 18. A method according to claim 16,further comprising repeating the second set of cascaded convolutionalneural networks (CNN) on the T1 image data after late gadoliniumenhancement (LGE).
 19. A method according to claim 14, furthercomprising identifying a myocardium region of the heart from theplurality of images and further identifying changes in myocardial T1image data by taking an average of respective T1 pixel valuesrepresenting the myocardium.
 20. A method according to claim 14, furthercomprising applying a dilated convolution into at least one of the firstset and the second set of cascaded convolutional networks.
 21. A systemcomprising: at least one processor; at least one memory device coupledto the processor and storing computer-readable instructions which, whenexecuted by the at least one processor, cause the system to performfunctions that comprise: acquiring magnetic resonance imaging data, fora plurality of images, of a heart of a subject; segmenting, usingcascaded convolutional neural networks (CNN), respective portions of theimages corresponding to respective epicardium layers and endocardiumlayers for a left ventricle (LV) and a right ventricle (RV) of theheart; extracting biomarker data from segmented portions of the images;and assessing hypertrophic cardiomyopathy from the biomarker data.
 22. Anon-transitory computer-readable medium having stored instructions that,when executed by one or more processors, cause a computing device toperform functions that comprise: acquiring magnetic resonance imagingdata, for a plurality of images, of a heart of a subject; segmenting,using cascaded convolutional neural networks (CNN), respective portionsof the images corresponding to respective epicardium layers andendocardium layers for a left ventricle (LV) and a right ventricle (RV)of the heart; extracting biomarker data from segmented portions of theimages; and assessing hypertrophic cardiomyopathy from the biomarkerdata.